Examples

It is recommended to use a Conda environment to run these examples. Download this yaml file and run the commands below to start using Jupyter Notebook.

$ conda env create --name my-gplately-env --file=env.yaml
$ conda activate my-gplately-env
$ jupyter notebook

Alternatively, you may use Docker to run these examples as well. Use -v option to access the local directory on the host machine. Visit this page for details.

$ docker pull gplates/gplately
$ docker run --rm -ti -v .:/ws -w /ws -p 8888:8888 gplates/gplately

Workflows

• 01 - Getting Started

  A brief overview of how to initialise GPlately’s main objects

• 02 - Plate Reconstructions

  Setting up a gplately.PlateReconstruction object, reconstructing geological data through time.

• 03 - Working with Points

  Setting up a gplately.Points object, reconstructing seed point locations through time. This notebook uses point data from the Paleobiology Database (PBDB).

• 04 - Velocity Basics

  Calculating plate velocities and plotting velocity vector fields.

• 05 - Working with Feature Geometries

  Processing and plotting assorted polyline, polygon and point data from GPlates 2.3’s sample data sets.

• 06 - Rasters

  Reading, resizing, resampling raster data, and linearly interpolating point data onto raster data.

• 07 - Plate Tectonic Stats

  Calculating and plotting subduction zone and ridge data (convergence/spreading velocities, subduction angles, subduction zone and ridge lengths, crustal surface areas produced and subducted, etc.).

• 08 - Predicting Slab Flux

  Predicting the average slab dip angle of subducting oceanic lithosphere.

• 09 - Motion Paths and Flowlines

  Using pyGPlates to create motion paths and flowines of points on a tectonic plate to illustrate the plate’s trajectory through geological time.

• 10 - Seafloor Grid

  Defines the parameters needed to set up a gplately.SeafloorGrid object, and demonstrates how to produce age and spreading rate grids from a set of plate reconstruction model files.

• 11 - Andes Fluxes

  Demonstrates how the reconstructed subduction history along the Andean margin can be potentially used in the plate kinematics analysis and data mining.

• 12 - Mutschler World Porphyry Copper Deposits Regional Plots

  Generates regional plots for Mutschler world porphyry copper deposits.

• 13 - Reconstructing Zircon Data

  Demonstrates how to reconstruct and plot Zircon data on a global map through geological time.

Note

All the Jupyter Notebook files of these sample workflows are available here in the GPlately GitHub repository.

Basics

• Hello World

  A minimal working example of GPlately.

• Use Plate Model Manager

  Use plate-model-manager to download plate reconstruction models.

• Plot with Cartopy

  Plot a paleo-map using Cartopy.

• Plot with PyGMT

  Plot a paleo-map using PyGMT.

• Reconstruct Files

  Reconstruct and plot shapefiles and other supported files.

• Use Your Own Plate Model

  Use your own plate model to reconstruct points.

• Save Reconstructed Geometries to Files

  Save the reconstructed data to shapefiles.

• Shortcut to Create PlateReconstruction and PlotTopologies Objects

  Easier way to get PlateReconstruction and PlotTopologies objects from the name of a plate reconstruction model.

• Generate Icosahedron Mesh

  Generate and visualize Icosahedron mesh.

Note

The Jupyter Notebook files of these basic examples are available here.